1. Field of the Invention
The present invention relates to object detection, and more particularly to methods and systems for object detection using cross-section analysis.
2. Description of the Related Art
Detection of objects is important in medical and non-medical applications. For example, medical image analysis and diagnosis depends on the ability to detect anatomical structures. Non-medical applications include 3-D facial recognition, such as computer methods that automatically locate human faces in 3-D images.
In object detection, the classes to be discriminated are not defined by the variations of the different objects themselves, but rather by distinguishing between “images containing the object” and “images not containing the object.” The existence of complex background structures significantly complicates the detection problem. It is very difficult to design a detection method to efficiently estimate a set of discriminating features that can differentiate the target objects from the complex background structures. In order to compute the values of differentiating features, the target objects need to be identified and separated out first, while in order to detect an object, the differentiating features need to be known.
In recent years, medical imaging has experienced an explosive growth due to advances in imaging modalities such as X-rays, computed tomography (CT), Doppler ultrasound, magnetic resonance (MR), positron emission tomography (PET) and single photon emission tomography (SPET). Two-dimensional (2-D) slices can be combined to generate a three-dimensional (3-D) volumetric model, and many images are now acquired directly as 3-D volumes. For example, low-dose helical CT can be applied as a modality for lung cancer screening. 3-D medical data can produce highly detailed images of internal anatomy having extremely complex formations, such as vessel trees in lung CT data.
FIG. 1 shows examples of medical images exhibiting compact round-shaped objects or nodules. Nodules are due to infections, inflammation, or tumors. Referring to FIG. 1, in the top row, a small square within each slice marks a region exhibiting a nodule, and the 3-D shape of the respective nodules is shown in the corresponding image in the lower row. The left-most and center images of FIG. 1 show nodules occluded by vessel trees. An example of a solid nodule is shown in the right column.
Object detection in 3-D volumetric data with complex non-target background structures is difficult due to the large quantity of data associated with each image, noise level and computational complexity. Many of the techniques that are considered suitable for object detection in 2-D do not have well-defined extensions or effective methods in 3-D. For example, segmentation of a convex curve segment is a relatively well-defined operation in 2-D. Its extension to 3-D, i.e., segmentation of a convex surface patch, is not an easy task, particularly when considering the variations in target objects and the noise on non-target structures. 3-D target objects can reside as geometric solids in volumetric data or targets may be occluded by background structures, which is a very difficult detection scenario to handle. The large quantity of information to be processed in 3-D volumetric data, in general, makes it impractical to select a detection technique that applies sophisticated, computationally expensive analysis to every position (voxel) in the 3-D volumetric data.
A number of techniques are available to compute 3-D shape features that can be used to differentiate compact round-shaped objects and objects with different shape properties. Tang provides a technique based on tensor voting to infer signs and directions of principal curvatures directly from 3-D data. Tang, C. and G. Medioni, G., “Curvature-augmented tensor voting for shape inference from noisy 3-D data,” In IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6):858-864, Jun. 2002. Paik provides a computer-aided detection (CAD) method called the surface normal overlap method that is applied to colonic polyp detection and lung nodule detection in helical CT images. Paik, D., Beaulieu, C., Rubin, G., Acar, B., Jeffrey, R., Yee, J., Dey, J. and Napel, S., “Surface Normal Overlap: A computer-aided detection method with application to colonic polyps and lung nodules in helical CT,” In IEEE Transactions on Medical Imaging, 23(6): 661-75 , Jun. 2004 Rieger provides a technique to estimate curvature of iso gray-level surfaces in gray-value images. Rieger, B., Timmermans, F., Vilet, L., and Verbeek, P., “On curvature estimation of ISO surfaces in 3-D gray-value images and the computation of shape descriptors,” In IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(8): 1088-94 , Aug. 2004 However, these techniques are not effective in the 3-D detection scenario, for example, because of lack of a well-defined region of interest, lack of robustness to noise, irregularity of the target object (difficult to estimate a consistent Gaussian curvature value), and computational costs.